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基于局部性正则化推广误差界的特征选择算法

, PP. 473-478

Keywords: 特征选择,局部性正则化推广误差界,流形结构,机器学习

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Abstract:

特征选择是当前模式识别领域的研究热点。滤波方法和封装方法是特征选择算法中评价特征子集的两种主要策略,但均不能保证其后所设计的分类器的推广性能。针对以上两种策略的不足,首先引入基于样本流形结构的局部性正则化推广误差界。并在此基础上,以局部性正则化推广误差界为评价函数,以局部性正则化分类方法为目标分类器,提出一种混合滤波-封装型特征选择算法。该算法既保持了较高的计算效率,又保证了目标分类器良好的推广性。实验结果表明,新算法具有比对比算法更优的分类性能。

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